Precise, pixel-wise geolocalization of astronaut photography is critical to
unlocking the potential of this unique type of remotely sensed Earth data,
particularly for its use in disaster management and climate change research.
Recent works have established the Astronaut Photography Localization task, but
have either proved too costly for mass deployment or generated too coarse a
localization. Thus, we present EarthMatch, an iterative homography estimation
method that produces fine-grained localization of astronaut photographs while
maintaining an emphasis on speed. We refocus the astronaut photography
benchmark, AIMS, on the geolocalization task itself, and prove our method's
efficacy on this dataset. In addition, we offer a new, fair method for image
matcher comparison, and an extensive evaluation of different matching models
within our localization pipeline. Our method will enable fast and accurate
localization of the 4.5 million and growing collection of astronaut photography
of Earth. Webpage with code and data at
this https URL

精确的像素级别的航天员摄影地理定位对于释放这种独特类型的遥感地球数据的潜力至关重要，特别是在灾害管理和气候变化研究中的应用。因此，我们提出了 EarthMatch，一种迭代单应性估计方法，可产生精细的航天员摄影地理定位，同时保持对速度的重视。

EarthMatch：航天员摄影细化定位的迭代配准

EarthMatch: Iterative Coregistration for Fine-grained Localization of  Astronaut Photography

Astronaut photography, spanning six decades of human spaceflight, presents a
unique Earth observations dataset with immense value for both scientific
research and disaster response. Despite its significance, accurately localizing
the geographical extent of these images, crucial for effective utilization,
poses substantial challenges. Current manual localization efforts are
time-consuming, motivating the need for automated solutions. We propose a novel
approach - leveraging image retrieval - to address this challenge efficiently.
We introduce innovative training techniques, including Year-Wise Data
Augmentation and a Neutral-Aware Multi-Similarity Loss, which contribute to the
development of a high-performance model, EarthLoc. We develop six evaluation
datasets and perform a comprehensive benchmark comparing EarthLoc to existing
methods, showcasing its superior efficiency and accuracy. Our approach marks a
significant advancement in automating the localization of astronaut
photography, which will help bridge a critical gap in Earth observations data.
Code and datasets are available at this https URL

本研究提出了一种新颖的方法，利用图像检索技术高效解决航天员摄影图像的地理位置定位问题，通过引入创新的训练技术，包括年度数据增强和一种中性感知多相似性损失，研发了高性能模型 EarthLoc，并通过与现有方法的全面基准测试，展示了其卓越的效率和准确性，该方法将填补地球观测数据中一个重要的空白。